Automated processing of medical data for disability rating determinations

ABSTRACT

Methods and systems are described for automated processing of medical data for insurance and disability determinations. Based on medical conditions claimed by the claimant, medical evidence queries are automatically generated to provide instructions to medical providers for conducting physical exams and laboratory tests and for retrieving medical records. After medical evidence is collected according to the queries, the medical evidence and related rating codes and decisions are displayed to rating personnel in a user-friendly format to assist in making a rating decision.

REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims priority benefitunder 35 U.S.C. § 120 from U.S. patent application Ser. No. 10/279,759,filed Oct. 23, 2002, which claims priority benefit under 35 U.S.C. §119(e) from U.S. Provisional Application No. 60/344,663, filed Oct. 25,2001, and U.S. Provisional Application No. 60/345,998, filed Oct. 24,2001.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods and systems for gathering andprocessing medical data to support rating decisions in the adjudicationof insurance and disability requests.

2. Description of the Related Art

Government agencies and insurance companies have developed rules foradjudication of insurance or disability requests. Examples of insuranceor disability programs include the Department of Veterans Affairs (VA)program, the Social Security Disability Insurance program, the Workers'Compensation program, various property and casualty insurance programs,and so forth.

In order to adjudicate a request made by a claimant, certain medicalevidence is required. Medical evidence requirements refers torequirements of information about a claimant that is relevant to themedical conditions claimed by the claimant, such as the age and genderof the claimant, physical examination data, laboratory test data andmedical history data pertinent to the claims, and so forth. Therequirements are specified by rules developed by the government agencyor by the insurance company, pertinent case law, government regulations,legislation and administrative decisions, and so forth. For example, therequirements may specify that if a claimant claims a certain medicalcondition, a medical provider must conduct certain physical examinationsand laboratory tests on the claimant or ask certain questions. Therequirements may also specify, for example, that a claimant must have arange of motions less than a certain degree to claim a limb disability.Requirements can also be specified by conventional medical knowledge,for example requiring a certain test to confirm a particular claimedcondition.

The rating rules are normally documented in manuals that may have manydifferent titles, herein referred to as “rating books.” A rating coderefers to a classification used by the government agency or insurancecompany that typically refers to a medical condition or a class ofmedical conditions in a rating book. The collection of rating rules,rating codes, pertinent legislation and case law for an insurance ordisability program is herein referred to as the “rules collection” forthat program. The rating rules may include rules on how to make a ratingdecision based on the collected medical evidence and the rating codes.For example, in a V.A. disability program, the rules collectiontypically specifies a disability percentage range based on rating codesand collected medical evidence. A V.A. rating personnel reviews therating codes and medical evidence, and specifies a disability percentagewithin the range.

In a disability or insurance request process, the claimant typicallyvisits a hospital, clinic or medical office. A medical provider such asa physician or a nurse collects medical evidence from the claimant tosupport a rating decision. The rating decision is typically made by thegovernment agency or the insurance company based on the medical evidencecollected by the medical provider and based on the rules collection. Themedical providers are typically provided with documents generallyreferred to as “physician's disability evaluation” or “medicalexamination handbooks” to assist them with collecting medical evidence.The handbooks are herein referred to as “medical handbooks.” The medicalhandbooks typically contain the medical evidence requirements for therules collection.

Whereas the rating books are typically intended for the rating personnelin the government agency or insurance company, the medical handbooks aretypically intended for the medical providers. Although they are somehowrelated, the rating books and medical handbooks typically contain veryfew direct cross-references. In addition, the medical providers oftenare not familiar with the rules collection of the insurance ordisability program, and make mistakes in using the medical handbooks.Therefore, the required medical evidence can be omitted or enteredincorrectly, thus affecting the making of a correct rating decision. Inaddition, the rating personnel, who typically have only limited medicalknowledge, must spend considerable time to review the medicalinformation collected by the medical providers. What is desired is anautomated system that provides instructions to medical providers tocollect medical evidence based on the rules collection of the insuranceor disability program. What is also desired is a system that providessupporting information in a user-friendly format to assist ratingpersonnel in making a rating decision based on the collected medicalevidence.

In many cases, a claimant makes claims for multiple medical conditions.The conventional practice is to complete a medical evidence document foreach claimed condition. This results in significant duplication ofeffort as duplicate medical data is gathered and identical medicalprocedures might be conducted multiple times. Therefore, what is desiredis a system that eliminates the duplications.

To better illustrate the drawbacks of conventional practices and theneed for better systems, the VA Compensation and Pension (C&P) programis described as an example. This government program provides payments ofbenefits to military veterans for medical disability resulting fromtheir military service. The rating rules are included in the Code ofFederal Regulations 38-CFR, the governing legislation, and in a ratingbook. The related medical handbook is a series of documents titledAutomatic Medical Information Exchange (AMIE) worksheets. Theseworksheets specify the medical evidence required and the procedures tobe utilized for each claimed condition included in 38-CFR. There arecurrently over fifty separate AMIE worksheets covering a wide array ofclaims, from a Prisoner of War Protocol Examination to ScarsExamination. Each worksheet is designed as a stand-alone medicaldocument for the particular claimed disability. In addition to the AMIEworksheets, legislatively mandated requirements, administrativerequirements, and court ordered information have, from time to time,specified other medical evidence or dictated the manner in which it isto be collected. Significant training and experience is required tofamiliarize medical providers with the worksheets and the additionalrequirements. Significant delays and extra cost in claims processing areencountered when required medical evidence is not provided or incorrectprocedures are conducted. Additionally, the claimant frequently claimsmultiple disabilities. These can number up to twenty or more claims forone claimant. The current practice is to complete an AMIE worksheet withall the requirements for each claimed disability. This results inunnecessary duplication of procedures with the entailed extra costs andtime.

SUMMARY OF THE INVENTION

One aspect of the invention relates to a method of assisting thecollection of medical evidence for the adjudication of a medicalinsurance or disability request. One or more claims of medicalconditions are received from a claimant. Based on the received claimsand based on a rules collection, a plurality of claimant-specificmedical evidence queries are generated. A plurality of instructions aregenerated based on the medical evidence queries. The instructions arethen used to collect the required medical evidence from physical exams,laboratory tests, medical records or claimant questionnaires.

Another aspect of the invention relates to a method of assisting theadjudication of a medical insurance or disability request. One or moreclaims of medical conditions are received from a claimant. Medicalevidence queries are generated based on the received claim and based ona disability rules collection. Each query is preferably associated witha rating code of the rules collection. Medical evidence is thencollected from the claimant according to the generated queries. A ratingreport that displays the collected medical evidence is created to assistrating personnel in making a rating decision. The rating report alsopreferably displays the rating codes associated with the displayedmedical evidence.

Still another embodiment relates to a medical disability claims benefitssystem. It includes a rules mapping component, a knowledge library, aclaimant-specific queries creation component and a protocol creationcomponent. The rules mapping component organizes a medical disabilityrules collection into at least a first plurality of general medicalevidence queries. The knowledge library stores the first plurality ofgeneral medical evidence queries. The claimant-specific query creationselects from the first plurality of general queries stored in theknowledge library a second plurality of claimant-specific medicalevidence queries based on at least one claimed medical condition of aclaimant. The protocol creation component creates at least one datacollection protocol based on the second plurality of queries. The systemcan also include a report creation component, which receives medicalevidence collected according to the at least one data collectionprotocol, and displays at least a portion of the received medicalevidence. These components can be implemented in computer instructions.They can be combined or separated into fewer or more components.

For purposes of summarizing the invention, certain aspects, advantagesand novel features of the invention have been described herein. Ofcourse, it is to be understood that not necessarily all such aspects,advantages or features will be embodied in any particular embodiment ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the general overview of one embodiment of adisability benefits claims system.

FIG. 2 illustrates one embodiment of an arrangement of modules andsub-modules.

FIG. 3 illustrates one embodiment of an arrangement of categories andsub-categories.

FIG. 4 illustrates one embodiment of a process of organizing rulescollection into a knowledge library.

FIG. 5 illustrates one embodiment of a process of generatingclaimant-specific medical evidence queries.

FIG. 6 illustrates one embodiment of a data entry form for claimedmedical conditions.

FIGS. 7A-7D illustrate one embodiment of a medical provider's examprotocol.

FIGS. 8A-8E illustrate one embodiment of a claimant questionnaire.

FIGS. 9A-9B illustrate one embodiment of a narrative medical report.

FIG. 10 illustrates one embodiment of a diagnostic code summary medicalreport.

FIGS. 11A-11H illustrate one embodiment of a rating report.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

To better illustrate the invention, certain embodiments of the inventionare described below in connection with the drawings. It should beunderstood that the scope of the invention is not limited by theseembodiments but defined by the claims.

Overview of Disability Benefits Claims System

FIG. 1 illustrates the general overview of one embodiment of adisability benefits claims system 100. The rules collection 112 for theinsurance or disability program and pertinent medical knowledge 114 areorganized by a FID mapping component 116 into a knowledge library 118.Based on the claimed medical conditions 120 from a claimant and based onthe rules collection or medical knowledge stored in the knowledgelibrary 118, the claimant-specific query creation module 122 createsclaimant-specific medical evidence queries.

The queries are then separated into medical record queries 124 formedical records, and exam queries 126 for physical exams and laboratorytests. The medical record queries 124 for medical records are used bythe clerk's protocol creation component 134 to create a clerk's datacollection protocol to collect the required data from medical records.The exam queries 126 for exams and tests are used by the medicalprovider's exam protocol creation component 136 to create a medicalprovider's data collection protocol to assist a physician, nurse ortechnician in conducting physical exams or laboratory tests. Thecomponent 136 may also use the exam queries 126 to create a claimant'squestionnaire to be answered by the claimant. The medical reportcreation component 142 uses the medical evidence collected from exams,tests, claimant questionnaire and medical records to create a medicalreport. The rating report creation component 152 creates a rating reportto assist rating personnel in adjudicating the claims. The collectedmedical evidence can be stored in a claimant database 160.

Organizing Rules Collection into Knowledge Library

Still referring to FIG. 1, component 112 represents the rules collectionfor the insurance or disability program, typically embodied in ratingbooks, legislation, administrative decisions and case law. Component 114represents pertinent medical knowledge, such as instructions to aphysician, lab technician or nurse for performing a physical exam orlaboratory test. The rules collection and medical knowledge areorganized by a FID mapping component 116 into FIDs and stored in aknowledge library component 118.

In a preferred embodiment, every unit of data that may be required bythe rules collection for making a rating decision is identified by afield identification number (FID). Examples of FID data fields include a“patient name” field, a “heart rate” field, a “impaired limb motionrange” field, and so forth. Each general medical evidence query isidentified by a FID. A general medical evidence query corresponds to amedical evidence requirement specified by the rules collection or bymedical knowledge. A claimant-specific medical evidence query isgenerated from the general medical evidence queries and based on theclaimant's claimed medical conditions. Claimant-specific queries aredescribed in the subsection titled “Generating claimant-specific medicalevidence queries”.

In a preferred arrangement, each FID includes a category code, a ratingcode and a data query code, separated by the underline symbol “_”. Forexample, a FID can take the form of “H047_SM500_T001”. The category code“H047” identifies the FID to a category of queries concerning the rightknee. The rating code “SM500” identifies the FID to a particular ratingcode for musculoskeletal injuries in a rating book. The data query code“T001” identifies the FID to the data query “What is the range ofmotion?.” In another example, the FID mapping number is“TK10_TS6600_T001”. The category code “TK10” represents a category ofqueries concerning bronchitis. The rating code “TS6600” represents arating book rating code “6600”. The data query code “T001” representsthe query “What is the FEV1 value?.” A query text table stores the dataquery codes and the query text for each of the data query codes. Thetable may also store a long instruction text for each data query code asan instruction or explanation. The stored query text and longinstruction text can be later displayed in a medical provider's examprotocol, claimant questionnaire, clerk's data collection protocol,medical report or rating report.

A FID can take other forms. For example, in a relational databasearrangement, a rating code table can store the rating code for each dataquery code, and a category code table can store the category code foreach data query code. Therefore a FID need only include a data querycode, and the rating code and category code for the FID can beidentified by referencing the rating code table and the category codetable. In an object-oriented arrangement, a FID can be an object thatincludes a data query object field, a rating code object field and acategory code object field.

The FID mapping component 116 organizes the rules collection into aplurality of FIDs. For example, for a rating code that identifiesdiabetes in a V.A. rules collection, the component 116 creates aplurality of FIDs, with each FID identifying a unit of medical evidencerequired for making a rating decision on the diabetes claim. Each FIDpreferably includes a category code, the V.A. rating code thatidentifies diabetes, and a data query code. For example, one FIDincludes a data query code representing the data query “Have you servedin the Vietnam War?” because V.A. rules assume that Vietnam veterans'diabetes conditions are caused by exposure to Agent Orange. As describedabove, the data query code may be further associated with a longinstruction text “If claimant has served in Vietnam and suffers fromdiabetes, assume that service connection exists.”

Arrangement of Modules and Sub-Modules, Categories and Sub-Categories

FIG. 2 illustrates one embodiment of a disability benefits claimssystems 100 that includes modules and sub-modules. A rating booktypically classifies medical conditions into disease systems, alsocalled body systems. Typical disease systems may include thecardiovascular system, the respiratory system, infectious diseases, andso forth. Some rating books classify a disease system into one or moresub-disease systems or sub-body systems. For example, a “cardiovasculardisease system” may include sub-disease systems such asmyocardial-infarction sub-disease system, arrhythmia sub-disease system,and so forth. A sub-disease system is typically unique to one diseasesystem and is not shared by multiple disease systems.

As shown in FIG. 2, each sub-disease system is mapped to one or moremodules of the disability benefits claims system 100. A modulerepresents a function within the sub-disease system. For example, thelung sub-disease system can be mapped to a “history of symptoms” module,a “history of general health” module, a “physical examination of thelungs” module, and so forth. In one embodiment, sub-disease systems canshare common modules. In one embodiment, modules are assigned prioritynumbers that identify a priority order among the modules.

Each module can include one or more sub-modules. For example, a “vitalsigns” sub-module can include data about the height, weight, pulse, andblood pressure of the claimant. A sub-module includes one or more FIDs.Modules can share common sub-modules. For example, the “vital signs”sub-module can be shared by multiple modules because vital signsinformation is needed for the diagnosis of many diseases and conditions.In one embodiment, sub-modules are assigned priority numbers thatidentify a priority order among the sub-modules.

A sub-module includes one or more FIDs. For example, the “vital signs”sub-module includes a “height” FID, a “weight” FID, a “pulse” FID and a“blood pressure” FID. In a preferred embodiment, each FID belongs toonly one sub-module. In one arrangement, each FID includes a sub-modulecode that identifies the sub-module of the FID. In another embodiment, asub-module table in the knowledge library 118 stores the FIDs for eachsub-module.

In other embodiments, modules and sub-modules are not introduced. Eachrating code and its general medical evidence queries directly correspondto a collection of FIDs. The FID collections for two rating codes mayshare one or more FIDs.

Referring to FIG. 3, the unique data elements that make up the rulescollection are grouped by category and sub-category. The categories andsub-categories preferably relate to classifications in the rating books.For example, categories can include “General”, “Complications”,“Function”, “Symptoms”, “Tests”, and so forth. A category can be furtherclassified into one or more sub-categories. For example, the “Function”category includes the sub-categories “ability” and “restriction”. The“Tests” category can include sub-categories “confirmation,” “essential,”“indication,” and “results.” A sub-category includes one or more FIDs.

FIG. 4 illustrates one embodiment of a process of organizing rulescollection into FIDs. From a start block 410, the process proceeds to ablock 420 to identify rating codes from the rating books for thedisability or insurance program. The process then proceeds to a block430 to identify data fields within each rating code. Each data fieldrepresents a general medical evidence query. Data fields may also beidentified based on pertinent medical knowledge, for example theknowledge of a experienced physician that certain medical evidence areneeded to make a rating decision for a particular rating code. Datafields may also be identified based on case law and administrativedecisions, for example the Deluca case and required “Deluca issues.”

The process then proceeds to a block 440 to group the data fields bycategory. In another embodiment, data fields are grouped bysub-category. The process proceeds to a block 450, where a FID isassigned to each data field. In a preferred embodiment, a category code,a rating code and a data query code is assigned to each FID. Thecategory code represents the category the data field is grouped into.The rating code represents the rating code for the data field. The dataquery code represents the data query for the medical evidence query. Theprocess then proceeds to a block 460 to store the FIDs in a knowledgelibrary component 118. The process terminates at an end block 470.

Generating Claimant-Specific Medical Evidence Queries

In FIG. 1, the claimant-specific query creation module 122 receives theclaimed medical conditions 120 from the claimant, and createsclaimant-specific medical evidence query based on the claims and byreferring to the general medical evidence queries stored in theknowledge library 118. FIG. 5 illustrates one embodiment of thequery-creation process.

Referring to FIG. 5, the process starts from a start block 510 andproceeds to a block 520, where the query creation component 122 receivesone or more claims of medical conditions from the claimant. In oneembodiment, the component 122 also receives other information providedby the claimant, for example information such as claimant name, age,gender filled out by the claimant on a data entry form form. FIG. 6 isan example data entry form. It can be filled out by the claimant or by aclerk. The “Special Instructions to the Doctor” section displays specialinstructions retrieved from the knowledge library 118 for the particularinsurance or disability program and displayed as a reminder to themedical provider.

Referring back to FIG. 5, at a block 530, the component 122 identifiesthe related modules based on the received claims. For example, if theclaimed condition is “loss of eyesight,” the component 122 may identifya “physical exam” module and a “neurological exam” module. Therelationships of medical conditions and related modules are stored inthe knowledge library 118. The component 122 also identifies allsub-modules of the identified modules. If two of the identified modulesshare common sub-modules, the duplicate sub-modules with the lowerpriority numbers are removed. From all of the FIDs that belong to theidentified modules, the duplicate FIDs can also be removed. In otherembodiments, instead of identifying the related modules based on thereceived claims, the component 122 identifies the related sub-modules,the related categories, or the related sub-categories. In anotherembodiment, the component 122 directly identifies the related FIDsstored in the knowledge library 118 based on the received claims.

At a block 540 of FIG. 5, the component 122 selects those FIDs in theknowledge library 118 that belong to the identified modules andsub-modules. The selected FIDs form a set of the claimant-specificmedical evidence queries. The set can be stored in a variety of formats,for example as a text string with FIDs separated by field delimiterssuch as colons or semicolons, as a text file with a FID in each line, asa table with each FID as a record, as a series of objects with each FIDhaving a “next FID” pointer that points to the next FID object, and soforth. In one embodiment, the component 122 compares the informationalready received from the claimant, and fills the related FIDs with suchinformation. For example, if the claimant has provided his or her name,age and gender, the component then fills the related FIDs with theclaimant-provided information. The details of filling a FID withcollected medical evidence are described below in more detail.

From the block 540, the process proceeds to a block 550, where thecomponent 122 determines which of the generated claimant-specificqueries may be satisfied from medical records. In another embodiment, ahuman operator reviews the generated queries and determines which of thequeries may be satisfied from medical records. In other embodiments,instead of determining on a per FID basis, the determination can also bemade on a per module, per sub-module, per category or per sub-categorybasis.

If all generated queries may be obtained from medical records, then theprocess proceeds to a block 570. Otherwise the process proceeds to ablock 560, where the component 122 generates a set of claimant-specificqueries to be satisfied from physical exams, claimant questionnaires, orlaboratory tests. At the block 570, the component 122 generates a set ofqueries whose results can be obtained from existing medical records. Theclaimant-specific queries generated at the block 540 are thus separatedinto two sets of queries. In another embodiment, the queries generatedat the block 540 are separated into three sets: one set of queries to besatisfied from physical exams and claimant questionnaires, another setto be satisfied from laboratory tests, and a third set to be satisfiedfrom medical records.

Referring back to the blocks 530 and 540 of FIG. 5, when the claimantsubmits claims for multiple conditions, it is possible that some of themodules are identified more than once by the claims. The component 122searches for duplicate modules and eliminates such duplications. Inother embodiments, the component can also search for and eliminateduplications on the sub-module or FID level.

Each module is associated with a priority number stored in the knowledgelibrary 118. In the case where multiple modules are called that examinethe same sub-disease system, the duplicate modules with the lowerpriority numbers are eliminated. In another embodiment, each FID isassociated with a priority number stored in the knowledge library 118.

In one embodiment, the generated queries can be updated by a humanoperator. For example, a medical provider or rating personnel reviewsthe generated claimant-specific queries and adds, modifies or deletesone or more queries. This allows some flexibility and human control inthe system 100. The human operator can also change the order ofgenerated claimant-specific queries determined by the priority numbers.

The component 122 also checks special rules stored in the knowledgelibrary 118 for exceptions and updates. Exceptions and updates aretypically caused by changes in legislation, case law, and insurance ordisability program rules. For example, special rules that represent theDeluca case decision can be stored in the knowledge library 118. Thestored Deluca special rules can be associated with FIDs, categories ormodules stored in the knowledge library 118. When the generatedclaimant-specific queries include a FID associated with a special rule,the special rule is retrieved from the knowledge library 118 and appliedto include a special rule instruction with the FID, or to add, modify orremove other claimant-specific queries. The special rule can also changethe order of generated claimant-specific queries determined by thepriority numbers.

Creating Medical Provider's and Clerk's Data Collection Protocols

Referring back to FIG. 1, based on the generated set ofclaimant-specific queries for exams, the medical provider's protocolcreation component 136 creates a medical provider's data collectionprotocol, also called a physician's exam protocol. The component 136 mayalso create a claimant questionnaire based on the claimant-specificqueries. Based on the generated set of claimant-specific queries formedical records, the clerk's protocol creation component 134 creates aclerk's data collection protocol.

FIGS. 7A-7D illustrate an example medical provider's data collectionprotocol. The protocol lists the claimant-specific medical evidencequeries to be satisfied from physician exams and laboratory tests. Inthe embodiment shown in FIGS. 7A-7D, the queries are grouped by categoryand sub-category. For example, the category “PHYSICAL EXAMINATION” shownin FIG. 7A includes sub-categories “VITAL SIGNS”, “HEENT”, “EYES”,“SKIN”, “HEART”, and “MUSCULOSKELETAL SYSTEM”. The grouping ofcategories and sub-categories presents the queries in a user-friendlyorder to the medical provider.

The medical provider uses the exam protocol to examine the claimant, andpreferably enters collected medical evidence into the protocol. In oneembodiment, the exam protocol is displayed to the medical provider onthe screen of an electronic device such as a computer or a personaldigital assistant, and the medical provider enters the collected medicalevidence corresponding to each query into the electronic device. Inanother embodiment, the exam protocol is displayed to the medicalprovider in a paper report, and the medical provider enters thecollected medical evidence on the paper report for a clerk to enter intoa computer system.

The medical evidence collected by the medical provider is then storedinto the disability claims benefits system 100. In one embodiment, foreach generated claim-specific query and its FID, the correspondingmedical evidence is simply inserted into the end of the FID. Forexample, for the FID “H047_SM500_T001” described above, if the medicalprovider determines that the range of motion is 90 degrees, then the FIDbecomes ““H047_SM500_T001_(—)90”, with the last field within the FIDstoring the value of the medical evidence. In another embodiment, atable includes a “original FID” field that stores the FID of each query,and a “data value” that stores the medical evidence value of thecorresponding FID. Other embodiments can also be implemented.

To replace or to supplement the physician's exam protocol, the component136 may create a claimant questionnaire for those queries that can besatisfied by collecting answers directly from the claimant. FIGS. 8A-8Eillustrate an example claimant questionnaire. The questionnaire can befilled out in paper or electronic form, by the claimant or by a clerkassisting the claimant. The questionnaire displays generatedclaimant-specific queries that can be satisfied by collecting answersfrom the claimant. The data entered into the questionnaire is thenstored as collected medical evidence corresponding to the displayedqueries. The data can be stored with the FIDs as described above, andpreferably displayed to the physician for review or verification.

The clerk's data collection protocol displays generatedclaimant-specific queries that are to be collected from medical records.For each query, the protocol preferably displays an instruction to theclerk, for example “retrieve data from previous x-ray charts.” Theinstructions can be retrieved from instructions stored in the knowledgelibrary 118 that are associated with stored general medical evidencequeries. In one embodiment, the disability benefits claims system isconnected to an electronic data storage that stores existing medicalrecords, for example the claimant database 160. The system thenautomatically searches the storage and collects required medicalevidence from the storage. In another embodiment, the systemautomatically notifies a custodian of medical records via email, voicemail or paper report to search for the medical evidence specified by thequeries.

Follow-Up Queries Based on Collected Medical Evidence

The query creation component 122 may create conditionalclaimant-specific queries. For example, if a required exam reveals anabnormal condition, then additional medical evidence may be requiredaccording to the rules collection 112 or according to medical knowledge114. Such additional medical evidence queries are called conditionalqueries. The query whose medical evidence may trigger the conditionalqueries is called a triggering query. A triggering query may beassociated with one or more sets of conditional queries. For example, apositive result of a laboratory test for a triggering query requires afirst set of conditional queries, and a negative result may require asecond set of conditional queries.

In one embodiment, the FID of a triggering query stored in the knowledgelibrary 118 includes a list of the FIDs of the conditional queries. Inanother embodiment, each query is stored as an object in the knowledgelibrary 118, and a triggering query object includes pointers to point toits conditional query objects. In yet another embodiment, the FID of atriggering query includes a flag code to indicate it is a triggeringquery. A triggering query table includes a first field that stores theFID of a triggering query and a second field that stores the FIDs of thecorresponding conditional queries. In each embodiment, the knowledgelibrary 118 may also store a triggering rule that indicates under whatconditions the conditional queries are needed, for example “when thetriggering query returns a positive test result” or “when the triggeringquery's medical evidence is not available.”

Regardless of the storage embodiments, when the claimant-specific querycreation component 122 generates a triggering query as aclaimant-specific query, the conditional queries for the triggeringquery are preferably also generated as claimant-specific queries. Theprotocol creation components 134 and 136 identifies a triggering query,and preferably displays its corresponding conditional queriesimmediately following the triggering query. The medical provider's examprotocol, clerk's data collection protocol and claimant's questionnairepreferably include instructions to explain the triggering rules, forexample “if this test result is positive, then answer the followingquestions.”

The conditional queries can be displayed after the medical evidence forthe triggering query is collected. For example, a medical provider'sexam protocol is displayed to the medical provider on the screen of anelectronic device, and the medical provider enters collected medicalevidence into the electronic device. As the medical provider enters themedical evidence for a triggering query into the electronic device, thesystem 100 compares the entered medical evidence with the triggeringquery's triggering rule stored in the knowledge library 118, anddisplays the conditional queries according to the triggering rule. Ifthe conditional queries are to be collected from physical exams, theyare displayed on the electronic device or on an additional paper report.The conditional queries can also be displayed on a claimantquestionnaire or clerk's data collection protocol, in electronic orpaper form.

Creating Medical Report

Referring back to FIG. 1, after medical evidence is collected fromphysical examinations, laboratory tests, medical records and claimantquestionnaire, the collected medical evidence is used by a medicalreport creation component 142 to create a medical report. FIGS. 9A-9Band FIG. 10 illustrate two example medical reports. FIGS. 9A-9Billustrate a sample narrative report. It includes collected medicalevidence, for example medical history data and other data, in preferablya narrative form.

FIG. 10 illustrates a sample diagnostic code summary report. For aclaimed right knee medical condition, the report displays a summary ofclaimant-specific queries and medical evidences, and correspondingrating codes such as “5010” and “5003”. In one preferred embodimentdescribed above, the FID for each query includes a category code, arating code and a data query code. The rating code of the FID is thusdisplayed along with the collected medical evidence of the query. Thereport thus displays direct relationships of medical conditions, medicalevidence and rating codes.

The medical report can be used by medical providers to review theclaimant's medical evidence and to familiarize the medical providerswith the associated rating codes. The report can also be used by ratingpersonnel to review the claimant's medical evidence and associatedrating codes. In some embodiments, medical reports can be usedinterchangeably with rating reports, which are described below inconnection with FIGS. 11A-11H.

Depending on the insurance or disability program, reports of differentformats can be generated to conform to the commonly accepted format ofthe particular program. For example, the medical evidence queries can begrouped by disease system on a report for a first insurance program, andgrouped by module on another report for a second disability program.

Creating Rating Report

Referring back to FIG. 1, the rating report creation component 152creates a rating report to assist rating personnel to adjudicate theinsurance or disability requests of the claimant. FIGS. 11A-11Hillustrate an example rating report, also called a rating decisiontoolkit.

In one embodiment, the rating report creation component 152 alsorecommends a rating decision to the rating personnel. The ratingdecision can be generated based on a set of mathematical formulas, arule-based system, an expert system, a self-learning neural network, afuzzy logic system, and so forth. The recommended rating decision can begenerated in the form of a numerical value representing a disabilitypercentage, a numerical value representing the insurance benefits dollaramount, a binary value representing a decision to grant or deny aninsurance request, and so forth. As shown in FIG. 11C, for the ratingcode “5259”, the component 152 recommends a V.A. rating of “10”, i.e., adisability percentage of 10%. FIG. 11G displays a summary of all ratingcodes and corresponding recommended disability percentages, and arecommended combined disability percentage. The rating personnel canreview the rating report and accept, reject or modify the recommendedratings.

The disclosed disability claims benefits system 100 can be implementedin a variety of computer languages, commercial applications andoperating platforms. For example, the system can be implement in wholeor in part in Visual Basis, C, SQL, and so forth.

Certain aspects, advantages and novel features of the invention havebeen described herein. Of course, it is to be understood that notnecessarily all such aspects, advantages or features will be embodied inany particular embodiment of the invention. The embodiments discussedherein are provided as examples of the invention, and are subject toadditions, alterations and adjustments. Therefore, the scope of theinvention should be defined by the following claims.

1. A computer-implemented first method for facilitating adjudication ofa medical disability request, the first method comprising: receivinginto a storage medical evidence data concerning a claimant; andautomatically generating a disability rating report based on the medicalevidence data, wherein the medical evidence data is obtained by a secondmethod comprising the following steps: receiving into a storage at leastone claim by the claimant of a medical condition; automaticallygenerating a plurality of medical evidence queries based on the at leastone claim and based on a disability rules collection; and receivinganswers to the queries into a storage, said answers corresponding to themedical evidence data.
 2. The first method of claim 1, wherein thesecond method, by which the medical evidence data is obtained, furthercomprises displaying the generated medical evidence queries in at leastone data collection protocol.
 3. The first method of claim 1, furthercomprising: generating a recommended rating decision based on themedical evidence data; and displaying the recommended rating decision inthe disability rating report.
 4. The first method of claim 1, furthercomprising: generating a recommended disability percentage based on themedical evidence data; and displaying the recommended disabilitypercentage in the disability rating report.
 5. A medical disabilityclaims benefits system including a computer-readable medium havingstored thereon medical evidence data concerning a claimant, andcomputer-executable instructions configured to perform a first methodcomprising: generating a disability rating report based on the medicalevidence data, wherein the medical evidence data is obtained by a secondmethod comprising: receiving from the claimant at least one claim of amedical condition; generating a plurality of medical evidence queriesbased on the at least one claim and based on a disability rulescollection; and receiving answers to the queries, said answerscorresponding to the medical evidence data.
 6. The system of claim 5,wherein the second method further comprises displaying the generatedmedical evidence queries in at least one data collection protocol. 7.The system of claim 5, wherein the computer-readable medium has storedthereon computer-executable instructions configured to perform the firstmethod, the first method further comprising: generating a recommendedrating decision based on the medical evidence data; and displaying therecommended rating decision in the disability rating report.
 8. Thesystem of claim 5, wherein the computer-readable medium has storedthereon computer-executable instructions configured to perform the firstmethod, the first method further comprising: generating a recommendeddisability percentage based on the medical evidence data; and displayingthe recommended disability percentage in the disability rating report.